基于混合需求的供应链多级库存协同订货模型

针对供应链多级库存系统存在混合需求的情况,建立了基于混合需求的多级库存协同订货模型。该模型假设在供应链中只有最下游节点面临的需求是独立需求,而其他上游节点面临的需求都是与之相关的相关需求。由于相关需求是一种块状需求,其库存成本构成与独立需求明显不同。因此,通过对多级库存系统的库存成本构成进行重新分析,分别给出了需求确定时不允许缺货和允许缺货的协同订货模型。另外,还通过对安全库存的分析给出了需求不确定时的协同订货模型。最后,给出了模型求解的遗传算法,并进行了实例仿真分析,展示了这种协同订货模型在混合需求的供应链中的实用性。     

一种基于协同缓存的内容中心网络能耗优化策略

当前大多内容中心网络(Content-Centric Network,CCN)缓存决策策略研究都没有综合考虑请求热点、网络能耗、内容流行度和节点协同等相关要素。因此提出一种基于内容流行度的协同缓存策略来优化内容中心网络的能耗。该策略将CCN的一个自治区域网络中的所有内容路由器节点当作一个协同缓存组,并把协同缓存组中每个节点的缓存容量分为两部分,一部分用于自身节点和其他节点协同缓存内容;另一部分用于自身节点独立缓存本地最流行的内容,以提高协同缓存组中内容副本种类的多样性,从而减少网络中内容的重复传输,实现网络能耗的优化。建立相关的能耗优化模型,采用一种改进的遗传算法求解出该协同缓存组能耗优化问题的最优解。实验结果表明,与相关文献中的缓存决策策略相比,所提策略可以有效地降低CCN的能耗,提高其可扩展性,进而指导CCN的演化和部署。     

面向产业链云服务平台的分布式备件库存协同控制方法与软件工具研究

针对制造业产业链协同服务平台的备件业务协作需求,提出跨节点的库存协同解决方案并建立近期需求预测计算模型。结合分布式节点企业的历史交易数据、库存数据的实时采集与处理应用,保障库存控制方案的实效性。采用MapReduce框架对模型参数计算过程进行优化,提高运算速度。基于遗传算法获取模型计算最优解,并将模型计算结果推送至下游经销商企业群,由反馈信息控制订单的动态生成。并将该模式应用在汽车产业链云服务平台,压缩了产业链响应时间。     

两阶段需求相关的动态易逝品最优订货策略

以动态市场环境下的易逝品为研究对象,针对两阶段需求相关、每次订货时带有固定的订购费用以及未满足需求部分延期供给等实际现象,建立了动态易逝品销售商两阶段订货决策模型.对模型的最优解进行了理论分析,并提出了寻求两阶段订货策略的简单方法.以朗科优盘经销商的采购实例来说明该模型的应用价值.最后,通过探讨订货决策与需求波动、两阶段需求相关系数以及延期供给率等因素的关系,得出了一些重要的管理启示.     

TBMC:一种基于多探测器协同的DTNs目标追踪策略

延迟容忍网络中的目标追踪问题一直被广泛关注,目前的相关策略主要围绕单探测器追踪单个目标问题。有些搜索策略利用节点相遇的可能性,有些利用节点的地理位置信息,有些采用更加新颖的方法,比如利用目标节点留下的移动方位信息和时间。这些策略都直接或者间接地利用了目标节点的信息进行目标搜索,但是普遍不高的成功率使得这些搜索策略应用范围受到一定限制。设计了一套基于多探测器协同的分布式目标追踪策略,通过远距离协同探测机制DTRs与直接协同机制TRs进行多探测器协同追踪,有效避免探测冲突与冗余步骤,提高追踪效率。理论分析与实验表明,该策略相比传统策略能有效减少追踪步数,提高目标追踪成功率。     

汽车行业制造供应链的IT应用

汽车行业是一个大规模协同的产业,它需要整车总装厂、一级供应商、二级供应商、售后服务网络等协同作业。客户需要通过公共媒体(包括互联网)浏览产品目录,根据自身不同的需求进行变形选择,向经销商订货;经销商将客户订第一供应层直至第n供应层,以及售后服务。这些都需要电子商务来连接供应链上的各个企业、各项业务,形成订单流程化,缩短订单处理的时间,使销售、制造与供     

基于时变需求的树型多级库存优化研究

考虑到战术仓库的需求率具有时变性的特点,应用供应链管理的思想研究了一个战役仓库、多个战术仓库组成的树型多级库存优化问题,建立了战役仓库采用延迟化订货策略、战术仓库分别采用等间隔或等量订货策略时的模型,并采用自适应遗传算法实现了模型的求解,实例验证了模型及算法的有效性.实验结果表明,战术仓库采用延迟订货策略,战术仓库采用等量订货策略时总费用最低.     

具有时变需求的配送型多级库存优化研究

考虑到战术仓库的需求率具有时变性的特点,应用供应链管理的思想研究了一个战役仓库、多个战术仓库组成的配送型多级库存优化问题,建立了战役仓库采用等量订货策略,战术仓库分别采用等间隔、等量订货策略时的模型,并采用自适应遗传算法实现了模型的求解,实例验证了模型及算法的有效性.实例表明：战役仓库采用等量订货策略,战术仓库采用等间隔订货策略时总费用最低.     

基于方案选择一致的指控节点间协同决策模型

针对指控节点的协同决策难以量化描述的问题,将该协同决策简化为对多个决策方案进行择优选取的过程.通过改进的Logistic方程对信息、决策主体能力和系统辅助能力对指控节点的态势感知质量影响进行描述,建立了指控节点的独立决策质量模型.遵循指控节点方案选择的一致性要求,综合考虑了影响协同决策的诸多因素,以影响单个指控节点在经过一次协同之后的决策结果状态为决策度量指标,构建了多指控节点的协同决策模型.通过多组随机试验,测试了该模型在各因素作用下的决策结果,为指控节点间的协同决策研究提供一种新的分析思路.     

软件协同模型中多模式可配置通道的研究

软件协同模型中的控制驱动模型很好地实现了计算与协同的分离,是一种重要的软件协同模型。通道在模型中表示协同部分,具有重要的地位。然而现有的控制驱动协同模型中,通道的设计较为简单,不能满足应用的需求。本文利用软件体系结构将连接子作为第一类实体的思想,提升通道在控制驱动协同模型的地位。首先对软件协同的独立要素进行分解,并使用Interceptor模型表示各个要素;然后对这些独立的协同要素进行组合,提出基于Interceptor的组合配置技术,以此对通道进行扩充,提出多模式可配置通道。多模式可配置通道表达能力得到加强,并且集成人员可以对其进行组合配置,以满足不同应用的需求。     

The benefit of VMI strategies in a stochastic multi-product serial two echelon system

We consider a multi-product serial two echelon inventory system with stochastic demand. Inventories at the downstream location are replenished periodically using an automatic ordering system. Under vendor managed inventory strategies the upstream stage is allowed to adapt these orders in order to benefit from economies of scale. We propose three different VMI strategies, aiming to reduce the order picking cost at the upstream location and the transportation costs resulting in reduced total supply chain costs. In a detailed numerical study the VMI strategies are compared with a retailer managed inventory strategy for two different demand models suitable for slow moving products. It is shown that if inventory holding costs are low, compared to handling and transportation costs, efficiencies at the warehouse are improved and total supply chain costs are reduced.     

A study on inventory replenishment policies in a two-echelon supply chain system

Inventory control plays an important role in supply chain management. Properly controlled inventory can satisfy customers’ demands, smooth the production plans, and reduce the operation costs; yet failing to budget the inventory expenses may lead to serious consequences. The bullwhip effect, observed in many supply chain management cases, causes excessive inventory due to information distortion, i.e. the order amount is exaggerated while a minor demand variation occurs, and the information amplified dramatically as the supply chain moves to the upstream. In this paper, one of the main causes of bullwhip effect, order batching, is considered. A simplified two-echelon supply chain system, with one supplier and one retailer that can choose different replenishment policies, is used as a demonstration. Two types of inventory replenishment methods are considered: the traditional methods (the event-triggered and the time-triggered ordering policies), and the statistical process control (SPC) based replenishment method. The results show that the latter outperforms the traditional method in the categories of inventory variation, and in the number of backlog when the fill-rate of the prior model is set to be 99%. This research provides a different approach to inventory cost-down other than the common methods like: information sharing, order batch cutting, and lead time reduction. By choosing a suitable replenishment policy, the number of backorder and the cost of inventory can be reduced.     

基于图卷积的双通道协同过滤推荐算法

在推荐系统中,利用图卷积网络等方法提取图的高阶信息缓解了冷启动问题。为了在此基础上融合神经网络协同过滤的深层特征提取能力,提出一种基于图卷积的双通道协同过滤推荐算法（GCNCF-2C）。首先,将推荐问题分为上游任务和下游任务;其次,在上游任务中,预训练编码器利用包含残差的一维卷积层和多个图卷积层在两个独立通道中对节点特征和图高阶特征进行分离提取,形成节点的特征表示;最后,解码器通过节点特征进行评级预测,进行端到端的训练。在数据集MovieLens-100K和MovieLens-1M上的实验表明,该算法相比于基线模型在两个数据集上的RMSE指标平均提高1.72%和1.76%,MAE指标平均提高2.7%和1.98%,同时在基于用户和项目的冷启动实验中RMSE指标平均提高5.9%,具有更好的综合性能。     

基于改进粒子群算法的异质车队二级IRP优化

针对供货商管理库存（Vendor Managed Inventory,VMI）模式下的二级库存路径问题（Inventory Routing Problem,IRP）,结合需求的波动特征,以二级库存路径问题系统总成本最小化为目标,建立异质车队的二级库存路径问题混合整数规划模型,并设计改进的粒子群算法对模型求解。数值实验分析验证了模型和算法的适用性和有效性,结果显示,使用异质车队不仅可以提高配送车辆的装载率,降低零售商的库存水平,还会减少二级库存路径问题系统总成本;敏感性分析表明,不论需求波动程度怎样变化,使用异质车队时二级库存路径问题系统总成本都会得到降低。     

Fuzzy knowledge-based token-ordering policies for bullwhip effect management in supply chains

The “Bullwhip Effect” is a well-known example of supply chain inefficiencies and refers to demand amplification as moving up toward upstream echelons in a supply chain. This paper concentrates on representing a robust token-based ordering policy to facilitate information sharing in supply chains in order to manage the bullwhip effect. Takagi–Sugeno–Kang and hybrid multiple-input single-output fuzzy models are proposed to model the mechanism of token ordering in the token-based ordering policy. The main advantage of proposed fuzzy models is that they eliminate the exogenous and constant variables from the procedure of obtaining the optimal amount of tokens which should be ordered in every period. These fuzzy approaches model the mentioned mechanism through a push–pull policy. A four-echelon SC with fuzzy lead time and unlimited production capacity and inventory is considered to survey the outcomes. Numerical experiments confirm the effectiveness of proposed policies in alleviating BWE, inventory costs and variations.     

随机需求下双渠道供应链库存动态交互优化

针对双渠道供应链库存系统导致的缺货与库存积压等问题，在线上线下均为随机需求的条件下，考虑生产延迟和物流延迟，建立了双渠道库存的单独控制、集中控制和交叉补货控制这三种模式的动态优化模型。首先，以库存动态微分方程为基础，创新性地以控制理论为指导思想，以泰勒展开和拉普拉斯变换为手段，得到双渠道库存系统的反馈传递函数；其次，考虑了交叉补货的进销存过程中的周期间交互、上下游间交互以及渠道间交互，利用延迟控制、反馈控制和比例-积分-微分（PID）控制构造了双输入、双输出的复杂交互系统，以此寻求双渠道库存系统自身以及渠道间的动态供需双平衡，优化双渠道库存持有量，降低缺货次数和缺货量并使其保持动态稳定状态；最后，通过数值仿真实验，对比三种双渠道库存控制策略。仿真结果表明，在线上线下渠道为不同分布的随机需求时，交叉补货控制的剩余库存比独立库存控制降低了4.9%，交叉补货控制的缺货率与独立控制和集中控制相比分别下降了66.7%和60%。实验结果表明，在线上线下渠道为不同分布的随机需求的情况下使用双渠道交叉补货策略能很好地降低库存持有量，减少缺货次数和缺货量，从而节约库存成本。     

随机需求下双渠道供应链库存动态交互优化

针对双渠道供应链库存系统导致的缺货与库存积压等问题,在线上线下均为随机需求的条件下,考虑生产延迟和物流延迟,建立了双渠道库存的单独控制、集中控制和交叉补货控制这三种模式的动态优化模型。首先,以库存动态微分方程为基础,创新性地以控制理论为指导思想,以泰勒展开和拉普拉斯变换为手段,得到双渠道库存系统的反馈传递函数;其次,考虑了交叉补货的进销存过程中的周期间交互、上下游间交互以及渠道间交互,利用延迟控制、反馈控制和比例-积分-微分（PID）控制构造了双输入、双输出的复杂交互系统,以此寻求双渠道库存系统自身以及渠道间的动态供需双平衡,优化双渠道库存持有量,降低缺货次数和缺货量并使其保持动态稳定状态;最后,通过数值仿真实验,对比三种双渠道库存控制策略。仿真结果表明,在线上线下渠道为不同分布的随机需求时,交叉补货控制的剩余库存比独立库存控制降低了4.9%,交叉补货控制的缺货率与独立控制和集中控制相比分别下降了66.7%和60%。实验结果表明,在线上线下渠道为不同分布的随机需求的情况下使用双渠道交叉补货策略能很好地降低库存持有量,减少缺货次数和缺货量,从而节约库存成本。     

The governing dynamics of supply chains: The impact of altruistic behaviour

This paper analyses an infinite horizon two-echelon supply chain inventory problem and shows that a sequence of the optimum ordering policies does not yield globally optimal solutions for the overall supply chain. First-order autoregressive demand pattern is assumed and each participant adopts the order-up-to (OUT) policy with a minimum mean square error forecasting scheme to generate replenishment orders. To control the dynamics of the supply chain, a proportional controller is incorporated into the OUT policy, which we call a generalised OUT policy. A two-echelon supply chain with this generalised OUT policy achieves over 10% inventory related cost reduction. To enjoy this cost saving, the attitude of first echelon player to cost increases is an essential factor. This attitude also reduces the bullwhip effect. An important insight revealed herein is that a significant amount of benefit comes from the player doing what is the best for the overall supply chain, rather than what is the best for local cost minimisation.     

Random network models and sensitivity algorithms for the analysis of ordering time and inventory state in multi-stage supply chains

Supply chains in reality face a highly dynamic and uncertain environment, especially the uncertain end-customer demands and orders. Since the condition of product market changes frequently, the tasks of order management, product planning, and inventory management are complex and difficult. It is imperative for companies to develop new ways to manage the randomness and uncertainty in market demands. Based on the graphical evaluation and review technique, this paper provides a simple but integrated stochastic network mathematical model for supply chain ordering time distribution analysis. Then the ordering time analysis model is extended so that the analysis of inventory level distribution characteristics of supply chain members is allowed. Further, to investigate the effects of different end-customer demands on upstream orders and relative inventory levels, model-based sensitivity analysis algorithms for ordering fluctuations and inventory fluctuations are developed. A detailed numerical example is presented to illustrate the application of the proposed models to a multi-stage supply chain system, and the results of which shows the effectiveness and flexibility of the proposed stochastic network models and algorithms in order and inventory management.